Method

Double Attention Net for Multimodal 3D Object Detection [DA-Net]
[Anonymous Submission]

Submitted on 15 May. 2023 04:49 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We proposes a fast yet effective backbone, termed
DCA-Net.
Parameters:
TBD
Latex Bibtex:

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 96.59 % 93.22 % 90.71 %
Car (Orientation) 96.58 % 93.14 % 90.56 %
Car (3D Detection) 90.73 % 82.29 % 77.90 %
Car (Bird's Eye View) 94.97 % 89.34 % 86.85 %
Pedestrian (Detection) 68.50 % 53.36 % 48.89 %
Pedestrian (Orientation) 43.83 % 34.06 % 31.06 %
Pedestrian (3D Detection) 53.20 % 42.78 % 38.67 %
Pedestrian (Bird's Eye View) 56.48 % 45.96 % 41.73 %
Cyclist (Detection) 86.64 % 75.64 % 71.06 %
Cyclist (Orientation) 86.16 % 75.11 % 70.49 %
Cyclist (3D Detection) 80.36 % 64.98 % 60.40 %
Cyclist (Bird's Eye View) 84.10 % 69.04 % 64.39 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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